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A new method to control error rates in automated species identification with deep learning algorithms.
Sébastien Villon1,2, David Mouillot3,4, Marc Chaumont5,6
1MARBEC, Univ of Montpellier, CNRS, IRD, Ifremer, Montpellier, France. villon@lirmm.fr.
Scientific Reports
|July 5, 2020
Summary
This study introduces a new framework to control errors in Deep Learning Algorithms (DLAs) for ecological image analysis. By automatically setting species-specific thresholds, misclassification rates in fish identification were significantly reduced.
Area of Science:
- Ecology
- Computer Science
- Biodiversity Assessment
Background:
- Automated organism identification using Deep Learning Algorithms (DLAs) is crucial for processing ecological survey data from images and videos.
- A significant challenge in current DLAs is the difficulty in controlling and quantifying their error rates, hindering reliable biodiversity assessment.
- Existing methods often lack robust mechanisms for error management, leading to potential inaccuracies in large-scale ecological data analysis.
Purpose of the Study:
- To develop and validate a novel framework for controlling the error rate of DLAs in ecological image identification.
- To introduce a method for automatically computing species-specific confidence thresholds to refine DLA outputs.
- To enhance the accuracy and reliability of biodiversity assessments derived from image-based ecological surveys.
Main Methods:
- A framework was developed to compute confidence thresholds for each species using a training dataset independent of the DLA training set.
- These species-specific thresholds were applied as a post-processing step to the DLAs' outputs.
- A new 'unsure' class was incorporated to categorize images where species identification confidence fell below the computed thresholds.
Main Results:
- The framework was applied to identify 20 fish species from 13,232 underwater images of coral reefs.
- Species misclassification rates decreased dramatically from 22% with raw DLAs to 2.98% after applying the post-processing thresholds.
- The method effectively reduced errors, demonstrating significant improvements in classification accuracy for ecological data.
Conclusions:
- The proposed framework offers a robust solution for controlling error rates in DLAs used for ecological data processing.
- This approach has the potential to overcome bottlenecks in extracting information from massive digital datasets, particularly in biodiversity assessment.
- Implementing these confidence thresholds ensures a high level of accuracy, making DLAs more reliable for scientific applications in ecology.
